Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

Veronica Piccialli and Antonio M. Sudoso
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Veronica Piccialli: Department of Civil and Computer Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
Antonio M. Sudoso: Department of Civil and Computer Engineering, University of Rome Tor Vergata, 00133 Rome, Italy

Energies, 2021, vol. 14, issue 4, 1-16

Abstract: Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. In this paper, we propose a deep neural network that combines a regression subnetwork with a classification subnetwork for solving the NILM problem. Specifically, we improve the generalization capability of the overall architecture by including an encoder–decoder with a tailored attention mechanism in the regression subnetwork. The attention mechanism is inspired by the temporal attention that has been successfully applied in neural machine translation, text summarization, and speech recognition. The experiments conducted on two publicly available datasets—REDD and UK-DALE—show that our proposed deep neural network outperforms the state-of-the-art in all the considered experimental conditions. We also show that modeling attention translates into the network’s ability to correctly detect the turning on or off an appliance and to locate signal sections with high power consumption, which are of extreme interest in the field of energy disaggregation.

Keywords: attention mechanism; deep neural network; energy disaggregation; non-intrusive load monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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